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Development with Glitch

Welcome to the Development section of Glitch - AI Personality Drift Simulator. This section provides comprehensive technical documentation for developers building and extending the platform.

🚀 Development Focus Areas​

Platform Architecture​

  • Modular Design: Extensible system for custom experiment designs
  • API Integration: RESTful APIs for programmatic experiment control
  • Real-time Monitoring: Live tracking of AI behavior changes
  • Scalable Infrastructure: Support for large-scale research deployments

Development Workflow​

  • Local Development: Set up your development environment quickly
  • Testing Framework: Comprehensive testing strategies and procedures
  • Code Quality: Linting, formatting, and best practices
  • Deployment: Production deployment and scaling considerations

🏗️ System Architecture​

Core Components​

  • API Server: FastAPI-based REST API for experiment control
  • LLM Integration: Ollama integration for local model inference
  • Vector Database: Qdrant for semantic search and storage
  • Redis Cache: High-performance caching and session management
  • Monitoring: Real-time experiment monitoring and alerting

Development Stack​

  • Backend: Python 3.12+, FastAPI, Pydantic
  • LLM: Ollama with local model support
  • Database: Qdrant vector database, Redis cache
  • Containerization: Docker & Docker Compose
  • Testing: pytest, coverage, integration tests

📚 Development Documentation​

Core Development Guides​

Quick Start for Developers​

# Clone and setup
git clone https://github.com/drKeeman/glitch_core
cd glitch-core
make setup

# Start development environment
make dev

# Run tests
make test

🔧 Development Tools​

Development Environment​

  • Docker Setup: Containerized development environment
  • Hot Reloading: Automatic code reloading during development
  • Debug Tools: Integrated debugging and profiling
  • Code Quality: Automated linting and formatting

Testing & Quality​

  • Unit Tests: Comprehensive test coverage
  • Integration Tests: End-to-end testing
  • Performance Tests: Load testing and optimization
  • Code Quality: Automated code review and quality checks

API Development​

  • RESTful APIs: Complete API documentation
  • OpenAPI Spec: Auto-generated API documentation
  • Authentication: Secure API access controls
  • Rate Limiting: API usage monitoring and limits

🛠️ Development Workflow​

Local Development​

  1. Environment Setup: Clone repository and configure environment
  2. Dependencies: Install Python dependencies and Docker services
  3. Database Setup: Initialize vector database and cache
  4. LLM Configuration: Set up Ollama with required models
  5. Testing: Run test suite to verify setup

Code Quality​

  • Linting: Automated code style checking
  • Formatting: Consistent code formatting
  • Type Checking: Static type analysis
  • Documentation: Auto-generated API docs

Testing Strategy​

  • Unit Tests: Individual component testing
  • Integration Tests: End-to-end workflow testing
  • Performance Tests: Load and stress testing
  • Security Tests: Vulnerability assessment

🔬 AI Research Platform Features​

Experiment Management​

  • Experiment Design: Tools for creating and configuring experiments
  • Parameter Control: Fine-grained control over drift simulation parameters
  • Data Collection: Automated data gathering and storage
  • Analysis Tools: Built-in analysis and visualization capabilities

Safety & Monitoring​

  • Real-time Monitoring: Live tracking of AI behavior changes
  • Safety Alerts: Immediate notification of concerning drift patterns
  • Audit Logging: Complete logging of all experimental activities
  • Rollback Capabilities: Ability to revert to stable states

API Integration​

  • RESTful APIs: Programmatic control of experiments
  • WebSocket Support: Real-time data streaming
  • Authentication: Secure API access controls
  • Rate Limiting: API usage monitoring and limits

🛡️ Security & Best Practices​

All development follows security best practices:

Security Features​

  • Code Review: All changes require peer review
  • Security Scanning: Automated vulnerability detection
  • Access Controls: Secure API authentication
  • Audit Logging: Complete activity logging

Development Standards​

  • Type Safety: Comprehensive type checking
  • Error Handling: Robust error management
  • Documentation: Comprehensive code documentation
  • Testing: High test coverage requirements

🤝 Contributing​

We welcome contributions from developers:

  • Bug Fixes: Report and fix issues
  • Feature Development: Add new capabilities
  • Documentation: Improve guides and tutorials
  • Testing: Enhance test coverage

See our Contributing Guide for details.

📞 Developer Support​

  • GitHub Issues: Report bugs and request features
  • Discord: Join our AI Safety Discord for developer discussions
  • Documentation: Comprehensive API and development guides

📋 Getting Started​

For New Developers​

  1. Read Architecture: Start with our Architecture Guide
  2. Set Up Environment: Follow the Getting Started Guide
  3. Explore APIs: Review the API Reference
  4. Run Tests: Use our Testing Guide

Development Resources​

Ready to start developing? Begin with our Getting Started guide to set up your development environment.